AI in Finance Summit New York schedule

For all the hype and promise of AI, financial services and other industries continue to see tremendous attrition between lifecycle stages from early AI ideation to model development to profitable deployment. Great ideas fall flat or get defunded, team volatility is endemic in high tech, fintech and “tech fin” companies alike and valid concerns about fairness and bias are driving regulatory focus on consumer backlash. This presentation will introduce the essential practice of “cognitive courtesy” with specific applications in product design, explainability and ModelOps, provide key lessons for user-centric design from the world of consumer electronics and equip you with strategies for attracting and retaining talent in challenging times.

-    The importance of “cognitive courtesy” to enable team alignment and create AI solutions that holistically work for end users
-    New ways to attract and retain AI talent
-    How to deliver MVPs when full model lifecycles can span quarters or years

 

Bjorn Austraat brings more than two decades of diverse experience in taking complex business problems and finding pragmatic, profitable solutions to them through machine learning, AI and other technologies.
Currently, he serves as SVP and Head of AI Acceleration at Truist where he is building out the new AI & Analytics Accelerator (A3) and AI COE to enable innovation and accelerate scalable solution deployment for AI and analytics across the enterprise.
Formerly, Bjorn was SVP for Agile AI at Wells Fargo, Global Cognitive Finance Leader with IBM for a top-3 International Bank and the Global Leader for Cognitive Visioning and Strategy for IBM Watson, where he provided strategic direction for marquee engagements including H&R Block and Vodafone.
Prior to joining IBM, Bjorn held a number of senior leadership roles in companies ranging from Silicon Valley startups to large, multinational consulting enterprises working with companies such as Apple, AT&T, Microsoft and Ford

- How can AutoML be applied to automate the decision-making process?
- How can these frameworks be created and deployed?
- What considerations need to be taken on board when automating such processes?

Henry is a co-founder at Snorkel AI. Before Snorkel AI, Henry was the tech lead for Facebook Applied AI’s representation learning team and spent his time in grad school building the Snorkel research library.

The capability of AI continues to mature rapidly and financial services organizations are gaining competency. As companies look to increase their value, AI technologies such as machine learning can help optimize processes, drive new revenue and differentiate your offering. Diana will share insight into how AI/ML can become a key enabler for delivering on your strategy, provide examples of how working closely with the business can reveal transformative AI solutions, as well as provide strategies for retaining and attracting diverse talent.

 

-    Putting clients at the center of everything you do is critical when developing AI solutions. Businesses should look at AI as a tool to differentiate your product and provide new value to your customers – whether its’ a competitive edge, reduced risk, lower costs, etc.
-    It is important to work in partnership with the business if you want to deliver high impact solutions. It is critical you understand their needs and deploy solutions that can be commercialized. 
-    Industry-wide we need to increase our focus on recruiting and developing diverse talent. Initiatives like Women in AI creates a more diverse work culture and promotes the development of inclusive AI.

 

Diana serves as a Business Engagement Lead in the Advanced Solutions team within BNY Mellon and has more than 10 years of experience in the areas of strategy development, strategic initiative execution and technology prioritization. 

As a Business Engagement Lead, she is responsible for promoting the use of data science and artificial intelligence capabilities throughout the organization. In this role, she serves as an internal consultant to senior leadership and key stakeholders within the business to uncover business needs and propose solutions utilizing advanced digital solutions that will drive business growth, optimize operational processes, and improve the client experience.

Diana is passionate about developing female talent and is the co-chair of BNY Mellon’s Women in AI initiative, which aims to provides women working and interested in AI a forum to connect, learn and build confidence.

The next frontier in data and AI will bring about significant advancements in processing and analyzing data, with more sophisticated algorithms for natural language processing, image and speech recognition, and machine learning. There will also be a greater focus on the ethical and social implications of AI, including the impact of automation on jobs and the need for greater transparency and accountability in AI decision-making. Additionally, specialized hardware and software platforms for running AI models, such as specialized AI chips, quantum computing, and cloud-based AI services, will become more advanced and widely available.

In the financial services industry, these trends have the potential to transform the customer experience, risk management, and operational efficiency. AI and data analytics can be leveraged to provide personalized banking experiences and enhance fraud detection, leading to more tailored and secure financial products and services. Predictive analytics can help identify potential risks and recommend proactive risk management strategies. Automation can streamline manual processes and reduce operational costs, while blockchain-based solutions can improve transparency and security in areas such as payments and trade finance. These trends are expected to continue to shape the future of the financial services industry, driving innovation and improving customer satisfaction.

- What is the next chapter in the usage of Data in AI?    
- What is the next chapter in AI with respect to the framework of models, computational infrastructure, and social implications?
- How do these new trends in Data and AI improve customer experience, risk management, and operational efficiency in the financial services industry?

 

- How can AI be leveraged to improve business strategy and production?
- What challenges may need to be considered and how can you reduce the impacts of such?
- What are the building blocks of a successful AI adoption to ensure that the strategy is holistic and achievable?

- Exploring the current opportunities and challenges in terms of capturing ROI when developing and deploying AI within BFSI
- How can possible areas for improvement in AI pipelines be identified?
- How can we optimize engineering, infrastructure, culture, teams and decision-making to see increased ROI (higher revenue, cost reduction, operational efficiency, value gains)?

Ercan Ucak is a Vice President within the Tech Strategy group of Cerberus Technology Solutions ("CTS"). Mr Ucak began his career as a Data Scientist in the Defense / Counter-intelligence and eventually Commercial space, where he leveraged his background in Statistics to develop ML / AI models to enable predictive analytics. He eventually joined CTS on Nov.19, utilizing his deep technical expertise to deploy Data & Analytics tools to create Enterprise Value.

- Discussion of the advantages of using low-code and no-code platforms, including faster development times and increased efficiency
- Overview of the challenges that may arise when implementing these technologies
- How low-code and no-code platforms can improve collaboration and communication within a financial organization
- Examining the security and compliance considerations that must be taken into account when using low-code and no-code platforms

 

- Understand the benefits of using synthetic data in reducing bias and increasing model robustness
- Learn how to evaluate the quality and diversity of synthetic data to ensure it is representative of the real-world distribution
- Explore the use of synthetic data in various financial applications, and why it can help with bias
- Discover how to integrate synthetic data with other techniques such as transfer learning and domain adaptation to improve model performance

Recent advances in machine learning and artificial intelligence suggest the emergence of a remarkable class of models that can learn a general representation of data to give rise to many downstream tasks. A general representation is the automatic transformation of data into a rich, abstract, reusable, and high-dimensional knowledge graph, which can be used to build many applications on top of, much more efficiently than building a specialized model from scratch for a single application. In this talk, I will describe how to design such models, why they work well (scaling law of deep learning) and how they could give rise to next generation of Al-enabled financial systems.

 

- What are Generalists AI Systems and why should they be considered?- How are these models designed and implemented?
- How could they could give rise to next generation of Al-enabled financial systems?

 

Ramin Hasani is a Principal AI and Machine Learning Scientist at the Vanguard Group and a Research Affiliate at the Computer Science and Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology (MIT). Ramin’s research focuses on robust deep learning and decision-making in complex dynamical systems. Previously he was a Postdoctoral Associate at CSAIL MIT, leading research on modeling intelligence and sequential decision-making, with Prof. Daniela Rus. He received his Ph.D. degree with distinction in Computer Science at Vienna University of Technology (TU Wien), Austria (May 2020). His Ph.D. dissertation and continued research on Liquid Neural Networks got recognized internationally with numerous nominations and awards such as TÜV Austria Dissertation Award nomination in 2020, and HPC Innovation Excellence Award in 2022. He has also been a frequent TEDx Speaker. 

 

Bayesian optimization is an approach for optimizing objective functions that are expensive to evaluate. “Vanilla” Bayesian optimization is typically best-suited for optimization over continuous domains of less than 20 dimensions and is tolerable to noisy function evaluations. It works by fitting a surrogate model to the objective function and quantifying uncertainty in the surrogate using a Bayesian machine learning technique, Gaussian process regression; we then build an acquisition function from this surrogate to decide where to sample. Although not limited to hyperparameter tuning, we will discuss how Bayesian Optimization is used to find optimal hyperparameters for machine learning models.

 

- What is Bayesian Optimization and how does it work?
- How can Bayesian Optimization be used to find optimal hyperparameters for machine learning models?
- What impact does this have within the financial sector?

 

A PhD candidate in Cornell University’s computer science department, Darian Nwankwo—an Atlanta, GA, native—is an enthusiastic problem-solver dedicated to applying his computational and mathematical skills to problems in various domains. Before beginning his matriculation at Cornell, he graduated top of his class from Morehouse College’s computer science department, earning membership into Phi Beta Kappa Honor Society along the way.
He has several industry and academic positions that have contributed to his diverse perspective on problem-solving. He was initially exposed to industry standards on how to write software while at Google. Subsequently, he decided to pursue academic research where he studied Human-Computer Interaction at Stanford university and Mathematical Biology at Morehouse College. After gaining exposure to work in industry and academia, he joined an industry research lab at Adobe, prior to starting his graduate studies.
His academic research afforded him a position with industry titan IBM where he worked with their Analog AI research team helping develop next generation hardware for accelerating AI applications. Upon completion, he was recognized by some researchers at AMD where he worked as a Scientific Machine Learning researcher, helping to advance our understanding on developing heterogenous systems for machine learning workloads.
In his spare time, Darian enjoys reading, learning new mathematics, playing pool and exercising. He is currently going through a curriculum on Quantitative Finance to learn how mathematics and computer science is leveraged in other disciplines to whet his curiosity. 

- How to create synthetic data and integrate it into your ML models
- An overview of synthetic data and its benefits for machine learning
- Best practices for creating high-quality synthetic data that accurately represents the real-world data distribution
- What are the ethical considerations of using synthetic data?

- Discover the importance of aligning your AI strategy with your overall business goals and how to identify which areas of your business could benefit most from AI implementation.
- What is the range of AI technologies available today and how to evaluate which ones are best suited for your specific use case
- Discuss the need for flexibility and adaptability in your AI strategy, including how to adjust your approach in response to changing market conditions, emerging technologies, and evolving customer needs.

Upal is a Product Owner at Fidelity Investments responsible for creating AI driven recommendation solutions that identifies a customer’s financial needs and deliver relevant experiences to help them find products and solutions aligned to these needs. 
In his role as a Product Owner, Upal leads a cross-functional team of data scientists, data engineers and analysts who helps create, deploy and measure the impact of these AI solutions. 
Upal brings over 12 years of experience in solving complex business problems through analytics, technology and data solutions. He is passionate about delivering measurable customer value through scalable AI capabilities.

Supreet is an AVP at Morgan Stanley. Prior to Morgan Stanley, she was a management consultant at ZS Associates where she automated different workflows and built data driven solutions for fortune 500 clients. She is extremely passionate about technology and AI and hence started her own community called DataBuzz where she engages the audience by sharing the latest AI and Tech trends and also mentors people who want to pivot in this field

Martin Ouko currently leads TIAA’s Model Lifestyle Management & Execution organization consisting of a highly skilled global team of Data, Pipeline and Model-Ops Engineers, tasked with designing, developing and productionizing elegant technology integrations and orchestrations that solve business problems and challenges of varying complexities. 
While not tinkering with cutting edge technology, he moonlights as Adjunct Professor, training future business leaders on subjects matters ranging from Economics to Business Management Concepts at a local college.
Outside industry, technology and academia, he serves on the board of Kiwimbi International, a Non-Profit organization dedicated to fundamentally altering the literacy culture in underserved communities around the world; one kid at a time.
In between these engagements, he can be found in the trees, sand bunkers or the rough, looking for his golf balls. And when he can’t find the fairways consistently, he prefers bringing together Charitable Organizations and philanthropists to bond over a good game under the banner of Prestige Invitational.

- Can blockchain platforms offer an alternative to platform governance?
- How could such platforms impact decision making processes?
- How would this then impact the regulation of such decisions?

 

George Samakovitis is Professor of FinTech and Deputy Head of School of Computing & Mathematical Sciences at the University of Greenwich, UK. George specialises in banking and payment systems technologies, Enterprise Architectures and AI for FinTech. His present research focuses on the deployment and governance of technologies for Anti-Money Laundering and Financial Crime, with particular emphasis on the use of DLT agents and development of blockchain infrastructure to deliver Collective Intelligence capabilities in FinTech networks.

George is presently a member of the Counter Fraud & Data Analytics Advisory Group of the HMG Cabinet Office and has served as a member of the FinCrime Working Group at the UK Payments Strategy Forum (2015-18), particularly working on KYC and Transaction Data Sharing and Analytics strategies and solutions for UK Financial Services. Most recently, he joined the BSI UK Data Standards Expert Panel, a diverse cross-sector panel of senior data executives, aiming to coordinate data standards interoperability across UK industry sectors.

George’s past work focused, among other, on banking technology investment decisions in economic booms and downturns, addressing, among other issues, the banking sector’s attitudes to uncertainty and risk under the disparate decision-making paradigms dictated by economic climate.

- Learn how advanced AI techniques can be used to create personalized consumer profiles for risk assessment and targeted marketing
- Using AI to create consumer profiles
How consumer profiles can improve consumer loyalty
- Understand the benefits of using machine learning and deep learning methods for personalized risk assessment
- Discover how to use AI-based techniques for targeted marketing strategies to improve customer engagement and increase revenue

-    How are you ensuring transparency and explainability in your models? 
-    How to convince your company of the importance of AI ethics
-    Identifying and minimizing biases in the dataset that may influence the model’s output

- What does open banking mean for data and analytics?
- How can AI improve the customer experience within open banking?
- What can we expect to see from FinTech in the near future and what impacts will this have?

 

Natesh is a Lead Data Scientist at Finicity where he creates Machine Learning products leveraging open banking data. Prior to this, he was a core member of the Machine Learning CoE at JPMChase and specialized in lending, fraud and marketing models.

- How AI can be used to automate various aspects of the payment process, such as verification, validation, and reconciliation.
- What are the benefits of using AI in the payment process? 
- Learn about the specific techniques and technologies used in AI-enabled payment systems, such as machine learning, natural language processing, and robotics process automation.
- How AI can be integrated with other technologies, such as blockchain, to further enhance the payment process's security, speed and scalability.

Today, ChatGPT, BARD and numerous Generative AI services gained popularity rapidly due to their impressive linguistic capability and high quality context-aware response.  This technology changes how people think of AI.  In Financial Sector, what does this technology advancement mean to us?  How does it change the way we think and work?  What do we need to consider when adopting and implementing this technology?  This session will explore the capabilities such as generating synthetic data and use it on document understanding and against fraud, and highlight the latest trends using Generative AI.

- Learn about the latest developments in generative AI and its applications in the banking sector
- Understand how generative AI can be used to improve financial forecasting, risk assessment and decision-making in banking
- Discover how generative AI can be used to create new financial products and services such as synthetic data, virtual assistants and personalization

John Chan is a Director of Technology at Raymond James Financial running the Carillon Labs - the innovation labs specialized in AI/ML.  His passion is to promote AI adoptions and implement machine learning solutions in Financial Sector.  He has 20+ years experience leading and implementing technology solutions from FinTech startups to top-tier banks and consulting firms.  Prior to Raymond James, John was an AI strategist and engineering lead at Gamma Lab of OneConnect Financial, Morgan Stanley data science team and KPMG Cognitive Technology Lab.  He is active in NLP research focusing on Generative AI, Conversational AI, Document Understanding and risk and compliance technology.

- How Neural Networks can help client retention
- Predicting client behaviour with the help of Neural Networks
- Neural networks and insurance claims

- How to build a strong DIS using AI 
- What are the positive impacts of a DIS on banking? 
- Improving software quality with a strong DIS 

- Overview of the current landscape of card and payments fraud, including common types of fraud and their impact on businesses and consumers
- Discussion of the potential of machine learning and other AI-based techniques for detecting and preventing fraud
- Examination of the challenges and limitations of using machine learning for fraud detection and ways to overcome them
- Explore Use-Cases of how banks and financial services are combatting card and payment fraud

It is now more crucial than ever for banks to keep up with trends and advancements in the field of fraud and credit abuse in order to manage and reduce their risks due to the rapidly evolving economic climate and technological landscape. Utilizing AI/ML to identify first and third party frauds in the credit and deposit accounts space, both pre and post book, as well as the detection and tagging of credit abuse at various granularities, has proven to be a game-changer in the finance risk industry. When it comes to exploiting AI/ML's potential in the finance risk sector, we have hardly even begun to scratch the surface given its dynamic nature, with fraud space being one of the early adopters.

 

- Why is it important for the financial risk sector to remain on top of the technological advancements and trends?
- How can AI/ML be utilised to both detect and prevent fraudulent activity?
- What advancements in this space can we expect to see?

 

Tanay Kulkarni is a data science enthusiast and a researcher working as an Applied AI/ML Senior Associate at JPMorgan Chase & Co.. Having worked on applications of AI in the product space, Tanay is now exploring the AI/ML innovations in the fraud space of the finance risk industry. Tanay, a major proponent of developing tailored methodologies to reach AI/ML's full potential, believes that custom frameworks must be created in the finance industry to fully utilize AI/ML.

-    How can you level up your existing anti-fraud systems?
-    What are the latest advancements of AI in the fraud detection industry?
-    What to expect in the coming years?